Kafka传输文件（字节数组）

使用Kafka以字节数组的形式传输文件

最近遇到解析大量小文件的需求，之前都是将文件放到HDFS，然后读取进行解析。

由于都是小文件且文件量很多，所以不想使用HDFS，于是采用Kafka来做中间件，效果还不错，特此分享。

原理是将文件以字节流的形式读入字节数组中，将字节数组发送到Kafka，供下游消费。

适用于海量小文件的处理。

实现

生产者

package com.upupfeng.kafka;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.io.File;
import java.io.FileInputStream;
import java.util.Properties;

/**
 * 将文件内容序列化，发到kafka中
 *
 * @author mawf
 */
public class SendFileToKafka {

    public static void main(String[] args) {

        String filePath = "D:\\dev\\a.xml.gz";

        Properties kafkaProps = new Properties();
        kafkaProps.put("bootstrap.servers", "server1:9092");
        kafkaProps.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        kafkaProps.put("value.serializer", "org.apache.kafka.common.serialization.ByteArraySerializer");
        KafkaProducer<String, byte[]> producer = new KafkaProducer<String, byte[]>(kafkaProps);
        try {
            File file = new File(filePath);
            FileInputStream fis = new FileInputStream(file);
            byte[] buffer = new byte[fis.available()];
            // 读到buffer字节数组中
            fis.read(buffer);
            ProducerRecord<String, byte[]> record = new ProducerRecord<String, byte[]>("dataTopic", file.getName(), buffer);
            producer.send(record);
            producer.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

消费者

package com.upupfeng.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.io.BufferedReader;
import java.io.ByteArrayInputStream;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.Properties;
import java.util.zip.GZIPInputStream;

/**
 * @author mawf
 */
public class ConsumerFileByteArrayFromKafka {

    public static void main(String[] args) {

        Properties props = new Properties();
        props.put("bootstrap.servers", "server1:9092");
        props.put("group.id", "group1");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        props.put("value.deserializer", "org.apache.kafka.common.serialization.ByteArrayDeserializer");
        KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<String, byte[]>(props);
        consumer.subscribe(Arrays.asList("dataTopic"));
        try {
            while (true) {
                ConsumerRecords<String, byte[]> records = consumer.poll(100);
                for (ConsumerRecord<String, byte[]> record : records) {
                    System.out.println("offset=" + record.offset() + ",key=" + record.key() + ",value=" + record.value());

                    String fileName = record.key();
                    byte[] message = record.value();

                    ByteArrayInputStream byteArrayInputStream = new ByteArrayInputStream(message);
                    GZIPInputStream gzipInputStream = new GZIPInputStream(byteArrayInputStream);

                    BufferedReader br = new BufferedReader(new InputStreamReader(gzipInputStream));
                    String line;
                    while ((line = br.readLine()) != null) {
                        System.out.println(line);
                    }

                    br.close();
                    byteArrayInputStream.close();
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            consumer.close();
        }

    }
}